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Journals / Mühendislik Bilimleri ve Tasarım Dergisi / 2026 / Cilt: 13 Sayı: 2

DEVELOPMENT OF MACHINE LEARNING MODELS FOR PROJECT EFFORT PREDICTION AND EXPLANATION USING SHAP METHOD

PROJE EFOR TAHMİNİ İÇİN MAKİNE ÖĞRENMESİ MODELLERİNİN GELİŞTİRİLMESİ VE SHAP YÖNTEMİ KULLANILARAK AÇIKLANMASI

Esma Nur Kaya(SİVAS CUMHURİYET ÜNİVERSİTESİ)0009-0000-3144-2686
Yasin Görmez(SİVAS CUMHURİYET ÜNİVERSİTESİ)0000-0001-8276-2030
Pages
528–544
Publication Date
2025-06-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results demonstrated that artificial neural networks, random forest, decision trees, and GradientBoost models performed most effectively across the datasets, with the decision tree identified as the best-performing model for effort estimation.

📌 3 Key Takeaways

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Objective & Hypothesis:In today’s digitalized world, successful project management has become essential for businesses, with accurate effort estimation emerging as a critical component due to the increasing prevalence of software projects.
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Key Findings & Data:Analyses were conducted on six datasets (china_original, cocomonasa_v1, humans2, nasa93, usp05, and usp05-ft) using 50 repeated holdout tests, and model performance was compared using metrics such as mean absolute error, mean squared logarithmic error, coefficient of determination, and mean relative magnitude error.
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Conclusion & Impact:The results demonstrated that artificial neural networks, random forest, decision trees, and GradientBoost models performed most effectively across the datasets, with the decision tree identified as the best-performing model for effort estimation.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Proje Yönetimi · Proje Efor Tahmini · Makine Öğrenmesi

Abstract

In today’s digitalized world, successful project management has become essential for businesses, with accurate effort estimation emerging as a critical component due to the increasing prevalence of software projects. Effort estimation facilitates cost optimization by predicting the time and labor required for project completion. This study developed and evaluated six regression models—random forest, decision tree, linear regression, neural network, GradientBoost, and AdaBoost—for project effort estimation. Analyses were conducted on six datasets (china_original, cocomonasa_v1, humans2, nasa93, usp05, and usp05-ft) using 50 repeated holdout tests, and model performance was compared using metrics such as mean absolute error, mean squared logarithmic error, coefficient of determination, and mean relative magnitude error. The results demonstrated that artificial neural networks, random forest, decision trees, and GradientBoost models performed most effectively across the datasets, with the decision tree identified as the best-performing model for effort estimation. Furthermore, the study utilized the SHAP (Shapley Additive Explanations) method to interpret the models, revealing that specific attributes were more influential than others in the decision-making process across different datasets.

Özet

Günümüzde işletmeler, dijitalleşen dünyaya uyum sağlamak için başarılı bir proje yönetimine ihtiyaç duymaktadır. Özellikle yazılım projelerinin artışıyla birlikte, doğru efor tahmini yapmak kritik bir süreç haline gelmiştir. Efor tahmini, projenin tamamlanması için gereken zaman ve iş gücü miktarını tahmin ederek maliyetleri optimize etmeyi sağlamaktadır. Bu çalışmada, proje efor tahmini için rastgele orman, karar ağacı, doğrusal regresyon, yapay sinir ağı, GradientBoost ve AdaBoost yöntemleri geliştirilmiştir. china_original, cocomonasa_v1, humans2, nasa93, usp05 ve usp05-ft gibi 6 farklı veri seti üzerinde 50 tekrarlayan sınama yaklaşımı kullanılarak analizler yapılmış ve modeller ortalama mutlak hata, ortalama logaritmik kare hatası, belirleme katsayısı ve ortalama göreli büyüklük hatası metrikleri kullanılarak karşılaştırılmıştır. Analiz sonuçlarına göre yapay sinir ağı, rastgele orman, karar ağaçları ve GradientBoost modellerinin farklı veri setlerinde en başarılı modeller olduğu gözlemlenmiştir. Proje efor tahmini için ise en başarılı modelin karar ağacı olduğu kanısına varılmıştır. Çalışmada yapılan diğer bir analizde ise, geliştirilen modeller açıklamalı yapay zekâ modeli olan SHAP (SHapley Additive exPlanations) yöntemi kullanılarak açıklanmıştır. Yapılan açıklamalar doğrultusunda her bir veri seti için bazı özniteliklerin model karar alma sürecinde diğer özniteliklere göre daha etkili olduğu gözlemlenmiştir.

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Cite This Article

Kaya, E. N., Görmez, Y. (2025). DEVELOPMENT OF MACHINE LEARNING MODELS FOR PROJECT EFFORT PREDICTION AND EXPLANATION USING SHAP METHOD. Mühendislik Bilimleri ve Tasarım Dergisi, Cilt: 13 Sayı: 2, 528–544. https://doi.org/10.21923/jesd.1604190

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